{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f9a46a3e-5755-4460-92bb-dfd3b0a86dab",
   "metadata": {},
   "source": [
    "## Look at example Quasar from SDSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8528c3e0-089c-4052-b02a-39333e1c1254",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings; warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eda472b2-e86c-43d9-8f0b-6d6839e53026",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# some peak detection code and 1 qso example, just see the picture\n",
    "import numpy as np; import matplotlib.pyplot as plt; from astroquery.sdss import SDSS; from specutils import Spectrum1D; from scipy.signal import find_peaks, savgol_filter; import astropy.units as u\n",
    "\n",
    "EMISSION_LINES = {'Lya': 1215.67, 'CIV': 1549.48, 'CIII]': 1908.734, 'MgII': 2799.117, 'Hb': 4861.33, 'Ha': 6562.80}\n",
    "LATEX_NAMES = {'Lya': r'Ly$\\alpha$', 'CIV': 'C IV', 'CIII]': 'C III]', 'MgII': 'Mg II', 'Hb': r'H$\\beta$', 'Ha': r'H$\\alpha$'}\n",
    "\n",
    "class QuasarLineDetector:\n",
    "\n",
    "    def __init__(self, plate, mjd, fiberID, z_catalog):\n",
    "        self.plate = plate; self.mjd = mjd; self.fiberID = fiberID; self.z_catalog = z_catalog; self.spectrum = None; self.deviations = {}\n",
    "\n",
    "    def load_spectrum(self):\n",
    "        \"\"\"Load SDSS spectrum with proper handling\"\"\"\n",
    "        specs = SDSS.get_spectra(plate=self.plate, mjd=self.mjd, fiberID=self.fiberID)\n",
    "        if not specs: raise ValueError(\"Spectrum not found\")\n",
    "        spec = Spectrum1D.read(specs[0], format=\"SDSS-III/IV spec\"); self.wavelength = spec.spectral_axis.value; self.flux = spec.flux.value\n",
    "        self.error = spec.uncertainty.array if hasattr(spec, 'uncertainty') else np.ones_like(self.flux) * 0.1\n",
    "        self.spectrum = {'wave': self.wavelength, 'flux': self.flux, 'err': self.error}\n",
    "        return self.spectrum\n",
    "\n",
    "    def advanced_line_detection(self):\n",
    "        \"\"\"Advanced line detection using multiple methods\"\"\"\n",
    "        results = {}\n",
    "        # 1. Continuum estimation using iterative sigma clipping\n",
    "        continuum = self._estimate_continuum_advanced()\n",
    "        continuum_subtracted = self.flux - continuum\n",
    "        # 2. Multi-scale wavelet detection\n",
    "        wavelet_peaks = self._wavelet_peak_detection(continuum_subtracted)\n",
    "        # 3. Matched filter detection for known lines\n",
    "        matched_peaks = self._matched_filter_detection(continuum_subtracted)\n",
    "        # 4. Bayesian peak detection\n",
    "        bayesian_peaks = self._bayesian_peak_detection(continuum_subtracted)\n",
    "        # 5. Combine detections\n",
    "        all_peaks = np.unique(np.concatenate([\n",
    "            wavelet_peaks.astype(int),\n",
    "            matched_peaks.astype(int),\n",
    "            bayesian_peaks.astype(int)\n",
    "        ]))\n",
    "        all_peaks = all_peaks.astype(int)\n",
    "        # 6. Line identification and characterization\n",
    "        for line_name, rest_wl in EMISSION_LINES.items():\n",
    "            obs_wl = rest_wl * (1 + self.z_catalog)\n",
    "            # Find peaks near expected position\n",
    "            tolerance = 0.05 * obs_wl  # 5% tolerance\n",
    "            mask = np.abs(self.wavelength[all_peaks] - obs_wl) < tolerance\n",
    "            line_peaks = all_peaks[mask]\n",
    "            if len(line_peaks) > 0:\n",
    "                # Select strongest peak\n",
    "                peak_idx = line_peaks[np.argmax(self.flux[line_peaks])]\n",
    "                # Calculate redshift from this line\n",
    "                z_line = self.wavelength[peak_idx] / rest_wl - 1\n",
    "                z_deviation = abs(z_line - self.z_catalog) / (1 + self.z_catalog)\n",
    "                if z_deviation > 0.02:  # 2% threshold\n",
    "                    # Reject this detection\n",
    "                    results[line_name] = {'detected': False}\n",
    "                    continue\n",
    "                # Characterize line\n",
    "                line_props = self._characterize_line(peak_idx, continuum_subtracted)\n",
    "                results[line_name] = {\n",
    "                    'detected': True,\n",
    "                    'wavelength': self.wavelength[peak_idx],\n",
    "                    'flux': self.flux[peak_idx],\n",
    "                    'snr': line_props['snr'],\n",
    "                    'fwhm': line_props['fwhm'],\n",
    "                    'ew': line_props['ew'],\n",
    "                    'z_line': z_line,\n",
    "                    'velocity_offset': line_props['velocity_offset'],\n",
    "                    'wavelength_error': line_props.get('wavelength_error', 0),\n",
    "                    'redshift_error': line_props.get('redshift_error', 0)\n",
    "                }\n",
    "            else:\n",
    "                results[line_name] = {'detected': False}\n",
    "        # Store deviations\n",
    "        self._store_deviations(results)\n",
    "        return results\n",
    "\n",
    "    def _store_deviations(self, line_results):\n",
    "        \"\"\"Store deviations for this object\"\"\"\n",
    "        object_id = f\"{self.plate}-{self.mjd}-{self.fiberID}\"\n",
    "        deviations = {\n",
    "            'z_catalog': self.z_catalog,\n",
    "            'line_deviations': {}\n",
    "        }\n",
    "        detected_lines = [r for r in line_results.values() if r['detected']]\n",
    "        if detected_lines:\n",
    "            z_deviations = []\n",
    "            for line_name, result in line_results.items():\n",
    "                if result['detected']:\n",
    "                    z_deviation = result['z_line'] - self.z_catalog\n",
    "                    deviations['line_deviations'][line_name] = z_deviation\n",
    "                    z_deviations.append(z_deviation)\n",
    "            deviations['mean_deviation'] = np.mean(z_deviations)\n",
    "            deviations['std_deviation'] = np.std(z_deviations)\n",
    "        else:\n",
    "            deviations['mean_deviation'] = None\n",
    "            deviations['std_deviation'] = None\n",
    "        self.deviations[object_id] = deviations\n",
    "\n",
    "    def _estimate_continuum_advanced(self, n_iter=5, sigma=3):\n",
    "        \"\"\"Advanced continuum estimation with iterative rejection\"\"\"\n",
    "        continuum = savgol_filter(self.flux, window_length=101, polyorder=3)\n",
    "        for _ in range(n_iter):\n",
    "            residuals = self.flux - continuum\n",
    "            std = np.std(residuals)\n",
    "            mask = np.abs(residuals) < sigma * std\n",
    "            x_good = self.wavelength[mask]\n",
    "            y_good = self.flux[mask]\n",
    "            continuum = np.interp(self.wavelength, x_good, y_good)\n",
    "            continuum = savgol_filter(continuum, window_length=101, polyorder=3)\n",
    "        return continuum\n",
    "\n",
    "    def _wavelet_peak_detection(self, flux, scales=[2, 4, 8, 16]):\n",
    "        \"\"\"Multi-scale wavelet transform for peak detection\"\"\"\n",
    "        from scipy import signal\n",
    "        all_peaks = []\n",
    "        for scale in scales:\n",
    "            points = min(len(flux), 20*scale)\n",
    "            t = np.arange(0, points) - (points - 1.0) / 2\n",
    "            t = t / scale\n",
    "            wavelet = (1.0 - t**2) * np.exp(-0.5 * t**2)\n",
    "            wavelet = wavelet / np.sqrt(np.sum(wavelet**2))\n",
    "            cwt_result = signal.convolve(flux, wavelet, mode='same')\n",
    "            peaks, _ = find_peaks(cwt_result, height=3*np.std(cwt_result))\n",
    "            all_peaks.extend(peaks)\n",
    "        unique_peaks = []\n",
    "        for p in sorted(set(all_peaks)):\n",
    "            if not any(abs(p - up) < 5 for up in unique_peaks):\n",
    "                unique_peaks.append(int(p))\n",
    "        return np.array(unique_peaks, dtype=int)\n",
    "\n",
    "    def _matched_filter_detection(self, flux):\n",
    "        \"\"\"Matched filter detection using line templates\"\"\"\n",
    "        peaks = []\n",
    "        templates = {\n",
    "            'narrow': self._gaussian_template(5),\n",
    "            'broad': self._gaussian_template(20),\n",
    "            'asymmetric': self._skewed_gaussian_template(10, 0.5)\n",
    "        }\n",
    "        for template_name, template in templates.items():\n",
    "            correlation = np.correlate(flux, template, mode='same')\n",
    "            correlation /= np.sqrt(np.sum(template**2))\n",
    "            template_peaks, _ = find_peaks(\n",
    "                correlation,\n",
    "                height=3*np.std(correlation),\n",
    "                distance=10\n",
    "            )\n",
    "            peaks.extend(template_peaks)\n",
    "        return np.unique(peaks)\n",
    "\n",
    "    def _bayesian_peak_detection(self, flux, prior_positions=None):\n",
    "        \"\"\"Bayesian approach to peak detection with data quality checks\"\"\"\n",
    "        flux_smooth = savgol_filter(flux, window_length=51, polyorder=3)\n",
    "        residuals = flux - flux_smooth\n",
    "        mad = np.median(np.abs(residuals - np.median(residuals)))\n",
    "        noise_std = 1.4826 * mad\n",
    "        if prior_positions is None:\n",
    "            prior_positions = [rest_wl * (1 + self.z_catalog)\n",
    "                               for rest_wl in EMISSION_LINES.values()]\n",
    "        peaks = []\n",
    "        for i, expected_pos in enumerate(prior_positions):\n",
    "            line_name = list(EMISSION_LINES.keys())[i]\n",
    "            center_idx = np.argmin(np.abs(self.wavelength - expected_pos))\n",
    "            window_size = 40\n",
    "            start = max(0, center_idx - window_size)\n",
    "            end = min(len(flux), center_idx + window_size + 1)\n",
    "            window = slice(start, end)\n",
    "            window_flux = flux[window]\n",
    "            window_wave = self.wavelength[window]\n",
    "            if len(window_flux) < 10:\n",
    "                continue\n",
    "            flux_range = np.max(window_flux) - np.min(window_flux)\n",
    "            if flux_range < 3 * noise_std:\n",
    "                continue\n",
    "            diff = np.diff(window_flux)\n",
    "            n_zeros = np.sum(np.abs(diff) < noise_std * 0.1)\n",
    "            if n_zeros > len(diff) * 0.5:\n",
    "                continue\n",
    "            if center_idx < 50 or center_idx > len(flux) - 50:\n",
    "                median_flux = np.median(window_flux)\n",
    "                if median_flux < 5 * noise_std:\n",
    "                    continue\n",
    "            max_idx_in_window = np.argmax(window_flux)\n",
    "            max_flux = window_flux[max_idx_in_window]\n",
    "            global_max_idx = start + max_idx_in_window\n",
    "            snr = max_flux / noise_std if noise_std > 0 else 0\n",
    "            local_continuum = np.median(window_flux)\n",
    "            peak_prominence = (max_flux - local_continuum) / noise_std\n",
    "            if snr > 2.0 and peak_prominence > 2.0:\n",
    "                if 2 < max_idx_in_window < len(window_flux) - 3:\n",
    "                    peaks.append(global_max_idx)\n",
    "        return np.array(peaks, dtype=int)\n",
    "\n",
    "    def _characterize_line(self, peak_idx, continuum_subtracted):\n",
    "        \"\"\"Detailed line characterization\"\"\"\n",
    "        start = max(0, peak_idx - 50)\n",
    "        end = min(len(self.flux), peak_idx + 50)\n",
    "        line_wave = self.wavelength[start:end]\n",
    "        line_flux = continuum_subtracted[start:end]\n",
    "        noise = np.std(continuum_subtracted[continuum_subtracted < 0])\n",
    "        snr = line_flux[peak_idx - start] / noise if noise > 0 else 0\n",
    "        half_max = line_flux[peak_idx - start] / 2\n",
    "        indices = np.where(line_flux > half_max)[0]\n",
    "        if len(indices) > 1:\n",
    "            fwhm_pixels = indices[-1] - indices[0]\n",
    "            fwhm_ang = line_wave[indices[-1]] - line_wave[indices[0]]\n",
    "        else:\n",
    "            fwhm_ang = 0\n",
    "        if len(line_flux) > 0:\n",
    "            ew = np.trapz(line_flux[line_flux > 0],\n",
    "                         line_wave[line_flux > 0])\n",
    "        else:\n",
    "            ew = 0\n",
    "        c_kms = 299792.458\n",
    "        velocity_offset = c_kms * ((self.wavelength[peak_idx] / (EMISSION_LINES.get('MgII', 2798.75) * (1 + self.z_catalog))) - 1)\n",
    "        if noise > 0 and snr > 0:\n",
    "            sigma_pixels = fwhm_pixels / (2.35 * snr) if fwhm_pixels > 0 else 1.0\n",
    "            dlambda = np.mean(np.diff(self.wavelength))\n",
    "            sigma_lambda = sigma_pixels * dlambda\n",
    "        else:\n",
    "            sigma_lambda = 0\n",
    "        return {\n",
    "            'snr': snr,\n",
    "            'fwhm': fwhm_ang,\n",
    "            'ew': ew,\n",
    "            'velocity_offset': velocity_offset,\n",
    "            'wavelength_error': sigma_lambda,\n",
    "            'redshift_error': sigma_lambda / self.wavelength[peak_idx] * (1 + self.z_catalog) if self.wavelength[peak_idx] > 0 else 0\n",
    "        }\n",
    "\n",
    "    def _gaussian_template(self, width):\n",
    "        \"\"\"Generate Gaussian template\"\"\"\n",
    "        x = np.arange(-3*width, 3*width + 1)\n",
    "        return np.exp(-0.5 * (x/width)**2)\n",
    "\n",
    "    def _skewed_gaussian_template(self, width, skew):\n",
    "        \"\"\"Generate skewed Gaussian template for asymmetric lines\"\"\"\n",
    "        x = np.arange(-3*width, 3*width + 1)\n",
    "        gaussian = np.exp(-0.5 * (x/width)**2)\n",
    "        skew_factor = 1 + skew * x / width\n",
    "        return gaussian * skew_factor * (skew_factor > 0)\n",
    "\n",
    "    def plot_results(self):\n",
    "        \"\"\"Comprehensive plotting of detection results\"\"\"\n",
    "        line_results = self.advanced_line_detection()\n",
    "        # Count detected lines\n",
    "        n_detected = sum(1 for r in line_results.values() if r['detected'])\n",
    "        # Get only detected lines for individual plots\n",
    "        detected_lines = []\n",
    "        for line_name, rest_wl in EMISSION_LINES.items():\n",
    "            if line_results[line_name]['detected']:\n",
    "                detected_lines.append((line_name, rest_wl))\n",
    "        # Create combined figure with 2 rows\n",
    "        if detected_lines:\n",
    "            n_cols = len(detected_lines)\n",
    "            fig = plt.figure(figsize=(5*n_cols, 4))\n",
    "            # First row: full spectrum (spanning all columns)\n",
    "            ax1 = plt.subplot2grid((2, n_cols), (0, 0), colspan=n_cols)\n",
    "            ax1.plot(self.wavelength, self.flux, 'k-', alpha=0.7, lw=0.5)\n",
    "            ax1.set_xlim(self.wavelength.min(), self.wavelength.max())\n",
    "            ax1.set_ylabel('Flux')\n",
    "            ax1.set_xlabel(r'Wavelength ($\\AA$)')\n",
    "            ax1.set_title(f'QSO {self.plate}-{self.mjd}-{self.fiberID}, z={self.z_catalog:.3f}, {n_detected} lines detected')\n",
    "            # Mark detected lines on full spectrum\n",
    "            for line_name, result in line_results.items():\n",
    "                if result['detected']:\n",
    "                    ax1.axvline(result['wavelength'], color='r', alpha=0.5, lw=1)\n",
    "                    display_name = LATEX_NAMES.get(line_name, line_name)\n",
    "                    ax1.text(result['wavelength'], ax1.get_ylim()[1]*0.9,\n",
    "                            display_name, rotation=90, va='top', fontsize=8)\n",
    "            # Second row: individual line plots\n",
    "            for i, (line_name, rest_wl) in enumerate(detected_lines):\n",
    "                ax = plt.subplot2grid((2, n_cols), (1, i))\n",
    "                det = line_results[line_name]\n",
    "                center_wl = det['wavelength']\n",
    "                window = max(150, 5 * det.get('fwhm', 30))\n",
    "                mask = np.abs(self.wavelength - center_wl) < window/2\n",
    "                wave_region = self.wavelength[mask]\n",
    "                flux_region = self.flux[mask]\n",
    "                if len(wave_region) > 0:\n",
    "                    ax.plot(wave_region, flux_region, 'k-', lw=1)\n",
    "                    ax.set_xlim(wave_region.min(), wave_region.max())\n",
    "                    # Mark detected peak\n",
    "                    ax.axvline(det['wavelength'], color='r', linestyle='--', lw=2,\n",
    "                              label=f'Detected z={det[\"z_line\"]:.4f}')\n",
    "                    # Mark expected position\n",
    "                    obs_wl = rest_wl * (1 + self.z_catalog)\n",
    "                    ax.axvline(obs_wl, color='b', linestyle=':', lw=1,\n",
    "                              label=f'Expected z={self.z_catalog:.4f}')\n",
    "                    # Add measurements text box\n",
    "                    text_lines = [\n",
    "                        f'SNR={det[\"snr\"]:.1f}',\n",
    "                        f'FWHM={det[\"fwhm\"]:.1f} $\\AA$',\n",
    "                        f'$\\Delta z$={det[\"z_line\"]-self.z_catalog:.4f}'\n",
    "                    ]\n",
    "                    if det.get('redshift_error', 0) > 0:\n",
    "                        text_lines[-1] += f'$\\pm${det[\"redshift_error\"]:.4f}'\n",
    "                    ax.text(0.05, 0.95, '\\n'.join(text_lines),\n",
    "                           transform=ax.transAxes,\n",
    "                           bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5),\n",
    "                           verticalalignment='top', fontsize=8)\n",
    "                ax.set_xlabel(r'Wavelength ($\\AA$)')\n",
    "                ax.set_ylabel('Flux')\n",
    "                display_name = LATEX_NAMES.get(line_name, line_name)\n",
    "                ax.set_title(f'{display_name} ({rest_wl:.1f} $\\AA$)')\n",
    "                ax.legend(fontsize=8, loc='upper right')\n",
    "                ax.grid(True, alpha=0.3)\n",
    "            plt.tight_layout()\n",
    "            plt.show()\n",
    "        else:\n",
    "            # If no lines detected, show only full spectrum\n",
    "            fig, ax1 = plt.subplots(1, 1, figsize=(16, 2))\n",
    "            ax1.plot(self.wavelength, self.flux, 'k-', alpha=0.7, lw=0.5)\n",
    "            ax1.set_xlim(self.wavelength.min(), self.wavelength.max())\n",
    "            ax1.set_ylabel('Flux')\n",
    "            ax1.set_xlabel(r'Wavelength ($\\AA$)')\n",
    "            ax1.set_title(f'QSO {self.plate}-{self.mjd}-{self.fiberID}, z={self.z_catalog:.3f}, No lines detected')\n",
    "            plt.tight_layout()\n",
    "            plt.show()\n",
    "        return fig, self.deviations\n",
    "\n",
    "plate, mjd, fiberID = 2238, 54205, 4; z_catalog = 2.04412; detector = QuasarLineDetector(plate, mjd, fiberID, z_catalog); spectrum = detector.load_spectrum()\n",
    "fig, deviations = detector.plot_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2adff6b-2050-40a6-ae55-000d10b8d6c8",
   "metadata": {},
   "source": [
    "## Group of QSOs from SDSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8ffca647-4f0a-4933-b910-dec6f2115b55",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f57879d3bb94a0390a90a713d0a48de",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing quasars:   0%|          | 0/5222 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Line deviation statistics (from catalog z):\n",
      "--------------------------------------------------\n",
      "CIV     : mean=-0.0032, std=0.0082, n=3460\n",
      "CIII]   : mean=-0.0014, std=0.0123, n=3460\n",
      "MgII    : mean=+0.0033, std=0.0111, n=3460\n"
     ]
    }
   ],
   "source": [
    "# It can work about 5 hours for fist time and download ~50gb, caching implemented\n",
    "EMISSION_LINES = {'CIV': 1549.48, 'CIII]': 1908.734, 'MgII': 2799.117} \n",
    "n_objects, z_min, z_max, object_class, min_snr = 10000, 1.5, 2.5, 'QSO', 20 # need to change to 10, no I need at least 20-25 SNR for good working conditions\n",
    "from astroquery.sdss import SDSS; import numpy as np; from tqdm.notebook import tqdm\n",
    "query = f\"SELECT TOP {n_objects} plate, mjd, fiberID, z, snMedian FROM SpecObj WHERE class = '{object_class}' AND z BETWEEN {z_min} AND {z_max} AND zWarning = 0 AND snMedian > {min_snr} ORDER BY z\"\n",
    "res = SDSS.query_sql(query); line_stats = {line: [] for line in EMISSION_LINES.keys()}\n",
    "for row in tqdm(res, desc=\"Processing quasars\"):\n",
    "    try:\n",
    "        detector = QuasarLineDetector(row['plate'], row['mjd'], row['fiberID'], row['z']); spectrum = detector.load_spectrum(); line_results = detector.advanced_line_detection()\n",
    "        detected_lines = {k: v for k, v in line_results.items() if v['detected']}\n",
    "        if len(detected_lines) >= 3:\n",
    "            for line_name, result in detected_lines.items(): line_stats[line_name].append(result['z_line'] - row['z'])\n",
    "    except: continue\n",
    "print(\"\\nLine deviation statistics (from catalog z):\"); print(\"-\" * 50)\n",
    "for line_name, deviations in line_stats.items():\n",
    "    if deviations: mean_dev, std_dev = np.mean(deviations), np.std(deviations); print(f\"{line_name:8s}: mean={mean_dev:+.4f}, std={std_dev:.4f}, n={len(deviations)}\")\n",
    "    else: print(f\"{line_name:8s}: no detections\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2c9d3c49-fa30-443d-9271-9b729ffc0178",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CIV: median Δz = -0.002749, mean Δz = -0.003239, n = 3460\n",
      "CIII]: median Δz = -0.000677, mean Δz = -0.001448, n = 3460\n",
      "MgII: median Δz = 0.003106, mean Δz = 0.003336, n = 3460\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Kruskal-Wallis H-test across lines:\n",
      "H-statistic = 884.1453, p-value = 1.0240e-192\n",
      "→ Statistically significant difference in Δz between emission lines.\n"
     ]
    }
   ],
   "source": [
    "# plot it\n",
    "import matplotlib.pyplot as plt; import numpy as np; from scipy.stats import kruskal\n",
    "plt.figure(figsize=(16, 3)); bins = np.linspace(-0.02, 0.02, 200); colors = ['#1f77b4', '#ff7f0e', '#2ca02c']; dz_arrays = {}\n",
    "for i, (line_name, dz_list) in enumerate(line_stats.items()):\n",
    "    if not dz_list: continue\n",
    "    dz_array = np.array(dz_list); dz_arrays[line_name] = dz_array; median_val = np.median(dz_array); mean_val = np.mean(dz_array)\n",
    "    print(f\"{line_name}: median Δz = {median_val:.6f}, mean Δz = {mean_val:.6f}, n = {len(dz_array)}\")\n",
    "    plt.hist(dz_array, bins=bins, alpha=0.07, label=f\"{line_name} (n={len(dz_array)})\", color=colors[i % len(colors)], edgecolor='black', linewidth=0.5)\n",
    "    plt.axvline(median_val, color=colors[i % len(colors)], linestyle='-', linewidth=1.5)\n",
    "    plt.text(median_val, plt.ylim()[1]*0.9, f\"{median_val:.5f}\", rotation=90, color=colors[i % len(colors)], ha='right', va='center', fontsize=8, fontweight='bold')\n",
    "    plt.axvline(mean_val, color=colors[i % len(colors)], linestyle='--', linewidth=1.5)\n",
    "    plt.text(mean_val, plt.ylim()[1]*0.8, f\"{mean_val:.5f}\", rotation=90, color=colors[i % len(colors)], ha='right', va='center', fontsize=8)\n",
    "plt.axvline(0, color='black', linestyle=':', linewidth=1.5, label='Expected (Δz = 0)'); plt.xticks(np.arange(-0.02, 0.02, step=0.0025)); plt.xlim((-0.02, 0.02))\n",
    "plt.xlabel(\"z_emp - z_catalog\"); plt.ylabel(\"Count\"); plt.legend(); plt.tight_layout(); plt.show()\n",
    "if len(dz_arrays) > 1:\n",
    "    stat, p_value = kruskal(*dz_arrays.values()); print(\"\\nKruskal-Wallis H-test across lines:\"); print(f\"H-statistic = {stat:.4f}, p-value = {p_value:.4e}\")\n",
    "    if p_value < 0.05:\n",
    "        print(\"→ Statistically significant difference in Δz between emission lines.\")\n",
    "    else:\n",
    "        print(\"→ No significant difference detected between the lines.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "759eb6ec-2e8c-4836-84dd-0f6d28bc6c3e",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log-Linear Correlation Coefficient: 1.0000\n",
      "Log-Linear Regression: Δz = 0.022791 * log10(wavelength) + -0.075454\n"
     ]
    }
   ],
   "source": [
    "# check log law\n",
    "rest_wavelengths = np.array([e for e in EMISSION_LINES.values()]); median_dz = np.array([-0.002749, -0.000677, 0.003106])\n",
    "import numpy as np; from scipy.stats import pearsonr, linregress\n",
    "log_rest_wavelengths = np.log10(rest_wavelengths); log_linear_corr, _ = pearsonr(log_rest_wavelengths, median_dz); print(f\"Log-Linear Correlation Coefficient: {log_linear_corr:.4f}\")\n",
    "slope_log, intercept_log, _, _, _ = linregress(log_rest_wavelengths, median_dz); print(f\"Log-Linear Regression: Δz = {slope_log:.6f} * log10(wavelength) + {intercept_log:.6f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0e4c11ee-d457-44f4-b0fa-79b5d56b84a7",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
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       "model_id": "89983aca39c84b018fa7744645776f72",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "z1.5-2.0 RA0-120 DEC-90-0:   0%|          | 0/194 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z=1.5-2.0 RA0-120 DEC-90-0: corr=0.988, slope=0.009499, valid=151/194\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d06ce3e86d548a79de65c8c67ddd1e5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "z1.5-2.0 RA0-120 DEC0-90:   0%|          | 0/494 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z=1.5-2.0 RA0-120 DEC0-90: corr=0.996, slope=0.007980, valid=382/494\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "47d77765d6a54d7cbf73cf12e1df0eee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "z1.5-2.0 RA120-240 DEC-90-0:   0%|          | 0/45 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z=1.5-2.0 RA120-240 DEC-90-0: corr=0.923, slope=0.011153, valid=35/45\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "23b68cb5a0ec4f339583f4ae2c162947",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "z1.5-2.0 RA120-240 DEC0-90:   0%|          | 0/1536 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z=1.5-2.0 RA120-240 DEC0-90: corr=0.999, slope=0.010562, valid=1174/1536\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "96be00bd2773434b91b6160718580797",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "z1.5-2.0 RA240-360 DEC-90-0:   0%|          | 0/70 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z=1.5-2.0 RA240-360 DEC-90-0: corr=0.985, slope=0.004724, valid=54/70\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "655b4247fd104abc92d93549037016af",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "z1.5-2.0 RA240-360 DEC0-90:   0%|          | 0/552 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z=1.5-2.0 RA240-360 DEC0-90: corr=0.996, slope=0.010755, valid=406/552\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d3913a72f7304573b42da75b557f6c55",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "z2.0-2.5 RA0-120 DEC-90-0:   0%|          | 0/116 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z=2.0-2.5 RA0-120 DEC-90-0: corr=0.990, slope=0.011488, valid=71/116\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "de1793b86dc847399fddb2f3280ec0e8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "z2.0-2.5 RA0-120 DEC0-90:   0%|          | 0/303 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z=2.0-2.5 RA0-120 DEC0-90: corr=0.994, slope=0.012776, valid=155/303\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a3f79fb2a61344f79d78500e87e15eb5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "z2.0-2.5 RA120-240 DEC-90-0:   0%|          | 0/62 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z=2.0-2.5 RA120-240 DEC-90-0: corr=0.536, slope=0.001611, valid=29/62\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b45f527562f149bd9e8d9b510be681a8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "z2.0-2.5 RA120-240 DEC0-90:   0%|          | 0/1410 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z=2.0-2.5 RA120-240 DEC0-90: corr=0.987, slope=0.009115, valid=774/1410\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3da929041ffa4708b61435576faf463f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "z2.0-2.5 RA240-360 DEC-90-0:   0%|          | 0/47 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z=2.0-2.5 RA240-360 DEC-90-0: corr=0.887, slope=0.009448, valid=21/47\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ba41d226ed9347e1ade44dfdba43991c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "z2.0-2.5 RA240-360 DEC0-90:   0%|          | 0/393 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z=2.0-2.5 RA240-360 DEC0-90: corr=0.998, slope=0.009243, valid=208/393\n",
      "\n",
      "Total: 12/12 valid, 3460 detections\n",
      "                         slope                 n_valid\n",
      "                          mean       std count     sum\n",
      "ra_center dec_center                                  \n",
      "60.0      -45.0       0.010493  0.001406     2     222\n",
      "           45.0       0.010378  0.003391     2     537\n",
      "180.0     -45.0       0.006382  0.006747     2      64\n",
      "           45.0       0.009838  0.001023     2    1948\n",
      "300.0     -45.0       0.007086  0.003340     2      75\n",
      "           45.0       0.009999  0.001069     2     614\n",
      "Detailed directional analysis:\n",
      "Direction | Mean slope | Std slope | Range | N measurements\n",
      "------------------------------------------------------------\n",
      "\n",
      "z = 1.75:\n",
      "  RA0-120 DEC-90-0: slope=0.009499, corr=0.988, n=151.0\n",
      "  RA0-120 DEC0-90: slope=0.007980, corr=0.996, n=382.0\n",
      "  RA120-240 DEC-90-0: slope=0.011153, corr=0.923, n=35.0\n",
      "  RA120-240 DEC0-90: slope=0.010562, corr=0.999, n=1174.0\n",
      "  RA240-360 DEC-90-0: slope=0.004724, corr=0.985, n=54.0\n",
      "  RA240-360 DEC0-90: slope=0.010755, corr=0.996, n=406.0\n",
      "\n",
      "z = 2.25:\n",
      "  RA0-120 DEC-90-0: slope=0.011488, corr=0.990, n=71.0\n",
      "  RA0-120 DEC0-90: slope=0.012776, corr=0.994, n=155.0\n",
      "  RA120-240 DEC-90-0: slope=0.001611, corr=0.536, n=29.0\n",
      "  RA120-240 DEC0-90: slope=0.009115, corr=0.987, n=774.0\n",
      "  RA240-360 DEC-90-0: slope=0.009448, corr=0.887, n=21.0\n",
      "  RA240-360 DEC0-90: slope=0.009243, corr=0.998, n=208.0\n",
      "\n",
      "=== SYSTEMATIC PATTERNS ===\n",
      "Northern hemisphere (DEC 0-90):\n",
      "  Mean slope: 0.010072\n",
      "  Std slope:  0.001673\n",
      "  Range:      0.007980 - 0.012776\n",
      "Southern hemisphere (DEC -90-0):\n",
      "  Mean slope: 0.007987\n",
      "  Std slope:  0.003950\n",
      "  Range:      0.001611 - 0.011488\n",
      "\n",
      "RA 60°: slopes = [0.00949928 0.00797978 0.01148752 0.01277605]\n",
      "\n",
      "RA 180°: slopes = [0.01115342 0.01056191 0.00161126 0.00911466]\n",
      "\n",
      "RA 300°: slopes = [0.00472419 0.01075488 0.00944804 0.00924346]\n",
      "\n",
      "=== VARIANCE ANALYSIS ===\n",
      "Within-direction variance: 0.00001206\n",
      "Between-direction variance: 0.00000654\n",
      "Variance ratio: 0.542\n",
      "\n",
      "Range of slopes within directions: 0.004002\n",
      "Overall range of slopes: 0.011165\n",
      "Ratio: 2.8x\n",
      "✓ DIRECTIONAL DEPENDENCE IS REAL - range between directions >> range within directions\n"
     ]
    }
   ],
   "source": [
    "# Does slope change with direction?\n",
    "import pandas as pd\n",
    "from tqdm.notebook import tqdm\n",
    "Z_START, Z_END, Z_STEP = 1.5, 2.5, 0.5\n",
    "N_OBJECTS, MIN_SNR, OBJECT_CLASS = 10000, 20, 'QSO'\n",
    "directional_data = []\n",
    "current_z = Z_START\n",
    "\n",
    "while current_z < Z_END:\n",
    "   from_z, to_z = current_z, current_z + Z_STEP\n",
    "   query = f\"SELECT TOP {N_OBJECTS} plate, mjd, fiberID, z, snMedian, ra, dec FROM SpecObj WHERE class = '{OBJECT_CLASS}' AND z BETWEEN {from_z} AND {to_z} AND zWarning = 0 AND snMedian > {MIN_SNR} ORDER BY z\"\n",
    "   res = SDSS.query_sql(query)\n",
    "   \n",
    "   if len(res) < 10:\n",
    "       current_z += Z_STEP\n",
    "       continue\n",
    "   \n",
    "   ra_bins = [(0, 120), (120, 240), (240, 360)]\n",
    "   dec_bins = [(-90, 0), (0, 90)]\n",
    "   \n",
    "   for ra_min, ra_max in ra_bins:\n",
    "       for dec_min, dec_max in dec_bins:\n",
    "           direction_mask = (res['ra'] >= ra_min) & (res['ra'] < ra_max) & (res['dec'] >= dec_min) & (res['dec'] < dec_max)\n",
    "           direction_objects = res[direction_mask]\n",
    "           \n",
    "           if len(direction_objects) < 10:\n",
    "               continue\n",
    "               \n",
    "           line_stats = {line: [] for line in EMISSION_LINES.keys()}\n",
    "           valid_count = 0\n",
    "           \n",
    "           for i in tqdm(range(len(direction_objects)), desc=f\"z{from_z:.1f}-{to_z:.1f} RA{ra_min}-{ra_max} DEC{dec_min}-{dec_max}\"):\n",
    "               row = direction_objects[i]\n",
    "               try:\n",
    "                   detector = QuasarLineDetector(row['plate'], row['mjd'], row['fiberID'], row['z'])\n",
    "                   spectrum = detector.load_spectrum()\n",
    "                   line_results = detector.advanced_line_detection()\n",
    "                   detected_lines = {k: v for k, v in line_results.items() if v['detected']}\n",
    "                   if len(detected_lines) >= 3:\n",
    "                       valid_count += 1\n",
    "                       for line_name, result in detected_lines.items():\n",
    "                           line_stats[line_name].append(result['z_line'] - row['z'])\n",
    "               except:\n",
    "                   continue\n",
    "           \n",
    "           mds = [np.median(deviations) for line_name, deviations in line_stats.items() if len(deviations) > 0]\n",
    "           if len(mds) >= 3:\n",
    "               median_dz = np.array(mds)\n",
    "               ln_rest_wavelengths = np.log(np.array(list(EMISSION_LINES.values())))\n",
    "               corr_ln, _ = pearsonr(ln_rest_wavelengths, median_dz)\n",
    "               slope_ln, intercept_ln, _, _, _ = linregress(ln_rest_wavelengths, median_dz)\n",
    "               \n",
    "               directional_data.append({\n",
    "                   'z_center': (from_z + to_z) / 2, 'ra_center': (ra_min + ra_max) / 2, 'dec_center': (dec_min + dec_max) / 2,\n",
    "                   'correlation': corr_ln, 'slope': slope_ln, 'n_objects': len(direction_objects), 'n_valid': valid_count\n",
    "               })\n",
    "               \n",
    "               print(f\"z={from_z:.1f}-{to_z:.1f} RA{ra_min}-{ra_max} DEC{dec_min}-{dec_max}: corr={corr_ln:.3f}, slope={slope_ln:.6f}, valid={valid_count}/{len(direction_objects)}\")\n",
    "   \n",
    "   current_z += Z_STEP\n",
    "\n",
    "directional_df = pd.DataFrame(directional_data)\n",
    "df_clean = directional_df[directional_df['correlation'] > 0.5]\n",
    "print(f\"\\nTotal: {len(df_clean)}/{len(directional_df)} valid, {directional_df['n_valid'].sum()} detections\")\n",
    "\n",
    "direction_stats = df_clean.groupby(['ra_center', 'dec_center']).agg({'slope': ['mean', 'std', 'count'], 'n_valid': 'sum'}).round(6)\n",
    "print(direction_stats)\n",
    "\n",
    "from scipy import stats\n",
    "direction_groups = [df_clean[df_clean['ra_center'] == ra]['slope'].values for ra in df_clean['ra_center'].unique()]\n",
    "print(\"Detailed directional analysis:\")\n",
    "print(\"Direction | Mean slope | Std slope | Range | N measurements\")\n",
    "print(\"-\" * 60)\n",
    "\n",
    "# Analyze each unique direction separately  \n",
    "for z_val in sorted(df_clean['z_center'].unique()):\n",
    "    print(f\"\\nz = {z_val:.2f}:\")\n",
    "    z_data = df_clean[df_clean['z_center'] == z_val]\n",
    "    \n",
    "    for _, row in z_data.iterrows():\n",
    "        ra_str = f\"RA{int(row['ra_center']-60)}-{int(row['ra_center']+60)}\"\n",
    "        dec_str = f\"DEC{int(row['dec_center']-45)}-{int(row['dec_center']+45)}\"\n",
    "        print(f\"  {ra_str} {dec_str}: slope={row['slope']:.6f}, corr={row['correlation']:.3f}, n={row['n_valid']}\")\n",
    "\n",
    "# Check for systematic patterns\n",
    "print(f\"\\n=== SYSTEMATIC PATTERNS ===\")\n",
    "north_data = df_clean[df_clean['dec_center'] > 0]  # Northern hemisphere\n",
    "south_data = df_clean[df_clean['dec_center'] < 0]  # Southern hemisphere\n",
    "\n",
    "print(f\"Northern hemisphere (DEC 0-90):\")\n",
    "print(f\"  Mean slope: {north_data['slope'].mean():.6f}\")\n",
    "print(f\"  Std slope:  {north_data['slope'].std():.6f}\")\n",
    "print(f\"  Range:      {north_data['slope'].min():.6f} - {north_data['slope'].max():.6f}\")\n",
    "\n",
    "print(f\"Southern hemisphere (DEC -90-0):\")\n",
    "print(f\"  Mean slope: {south_data['slope'].mean():.6f}\")\n",
    "print(f\"  Std slope:  {south_data['slope'].std():.6f}\")\n",
    "print(f\"  Range:      {south_data['slope'].min():.6f} - {south_data['slope'].max():.6f}\")\n",
    "\n",
    "# Check RA dependence\n",
    "for ra_center in sorted(df_clean['ra_center'].unique()):\n",
    "    ra_data = df_clean[df_clean['ra_center'] == ra_center]\n",
    "    print(f\"\\nRA {ra_center:.0f}°: slopes = {ra_data['slope'].values}\")\n",
    "\n",
    "# The smoking gun: variance analysis\n",
    "print(f\"\\n=== VARIANCE ANALYSIS ===\")\n",
    "overall_std = df_clean['slope'].std()\n",
    "within_direction_var = 0\n",
    "between_direction_var = 0\n",
    "n_directions = 0\n",
    "\n",
    "for (ra, dec), group in df_clean.groupby(['ra_center', 'dec_center']):\n",
    "    direction_mean = group['slope'].mean()\n",
    "    overall_mean = df_clean['slope'].mean()\n",
    "    \n",
    "    # Within-direction variance (if multiple measurements)\n",
    "    if len(group) > 1:\n",
    "        within_direction_var += group['slope'].var() * (len(group) - 1)\n",
    "    \n",
    "    # Between-direction variance\n",
    "    between_direction_var += len(group) * (direction_mean - overall_mean)**2\n",
    "    n_directions += len(group)\n",
    "\n",
    "within_direction_var /= (n_directions - len(df_clean.groupby(['ra_center', 'dec_center'])))\n",
    "between_direction_var /= (len(df_clean.groupby(['ra_center', 'dec_center'])) - 1)\n",
    "\n",
    "print(f\"Within-direction variance: {within_direction_var:.8f}\")\n",
    "print(f\"Between-direction variance: {between_direction_var:.8f}\")\n",
    "print(f\"Variance ratio: {between_direction_var/within_direction_var:.3f}\")\n",
    "\n",
    "# Range analysis\n",
    "direction_ranges = []\n",
    "for (ra, dec), group in df_clean.groupby(['ra_center', 'dec_center']):\n",
    "    if len(group) > 1:\n",
    "        direction_range = group['slope'].max() - group['slope'].min()\n",
    "        direction_ranges.append(direction_range)\n",
    "\n",
    "print(f\"\\nRange of slopes within directions: {np.mean(direction_ranges):.6f}\")\n",
    "print(f\"Overall range of slopes: {df_clean['slope'].max() - df_clean['slope'].min():.6f}\")\n",
    "print(f\"Ratio: {(df_clean['slope'].max() - df_clean['slope'].min()) / np.mean(direction_ranges):.1f}x\")\n",
    "\n",
    "if (df_clean['slope'].max() - df_clean['slope'].min()) / np.mean(direction_ranges) > 2:\n",
    "    print(\"✓ DIRECTIONAL DEPENDENCE IS REAL - range between directions >> range within directions\")"
   ]
  }
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